Computational Grounding of Responsibility Attribution and Anticipation in LTLf
This work addresses responsibility attribution for autonomous systems, but it is incremental as it builds on existing LTLf and reactive synthesis frameworks.
The paper tackles the problem of formalizing responsibility in autonomous systems by connecting it to reactive synthesis concepts, resulting in complexity characterizations and algorithms for attributing and anticipating responsibility.
Responsibility is one of the key notions in machine ethics and in the area of autonomous systems. It is a multi-faceted notion involving counterfactual reasoning about actions and strategies. In this paper, we study different variants of responsibility in a strategic setting based on LTLf. We show a connection with notions in reactive synthesis, including synthesis of winning, dominant, and best-effort strategies. This connection provides the building blocks for a computational grounding of responsibility including complexity characterizations and sound, complete, and optimal algorithms for attributing and anticipating responsibility.